SVM vs KNN in Machine Learning

Support Vector Machine(SVM) and K Nearest Neighbours(KNN) both are very popular supervised machine learning algorithms used for classification and regression purpose. Both SVM and KNN play an important role in Supervised Learning.

Support Vector Machine(SVM)

Support Vector Machine is a effective supervised machine learning algorithm used for classification and regression tasks. The main objective of SVM is to find an optimal hyperplane that best separates the data into different classes in a high-dimensional space. The hyperplane is chosen to maximize the margin, which is the distance between the hyperplane and the nearest data points (support vectors) of each class. Kernel functions are used for transforming lower dimensional input space to higher dimensional output.

Advantages of Support Vector Machine(SVM)

• Effective in High-Dimensional Spaces: SVM performs well in high-dimensional spaces, making it suitable for tasks with a large number of features.
• Robust to Overfitting: SVM has regularization parameters that help in avoiding overfitting. The margin maximization objective encourages a simple model that generalizes well to new, unseen data.
• Versatile Kernel Options: SVM allows the use of different kernel functions (e.g., linear, polynomial, radial basis function) to handle various types of data and relationships between features.
• Global Optimization: SVM’s training involves a convex optimization problem, which ensures that the solution found is the global optimum, providing a more reliable result.
• Effective in Nonlinear Data: With the use of kernel functions, SVM can handle non-linear relationships between features.

Disadvantages of Support Vector Machine(SVM)

• Computational Complexity: Training an SVM can be computationally expensive, especially for large datasets.
• Memory Intensive: SVMs can be memory-intensive, particularly when dealing with large datasets, as the algorithm needs to store all support vectors.
• Sensitivity to Noise: SVM is sensitive to noisy data, and outliers in the training set can significantly impact the performance.
• Choice of Kernel: Selecting an appropriate kernel and tuning its parameters can be challenging, and the performance of the SVM model is sensitive to these choices.
• Binary Classification: SVM is originally designed for binary classification. Extensions to handle multi-class problems involve combining multiple binary classifiers, which can complicate the training process.

K Nearest Neighbour(KNN)

KNN is a simple and a very effective supervised machine learning algorithm. It belongs to the family of instance-based, non-parametric algorithms, meaning it makes predictions based on the similarity of input data points. KNN basically makes predictions based on the similarity of data points in the sample space. The performance of KNN is basically based on the choice of K. KNN works by memorizing the entire training dataset. When a new data point is given for prediction, KNN looks at the k-nearest data points in the training set based on a specified distance metric (commonly Euclidean distance).

• For classification, it assigns the majority class among the k-nearest neighbors to the new data point.
• For regression, it predicts the average or weighted average of the target values of the k-nearest neighbors.

Advantages of K Nearest Neighbour(KNN)

• Simple Implementation: KNN is easy to understand and implement, making it suitable for quick prototyping.
• No Training Period: Since KNN is an instance-based learning algorithm, it doesn’t require a training phase. The model is built during the prediction phase.
• Versatility: KNN can be applied to both classification and regression tasks.
• Non-Parametric: KNN doesn’t make assumptions about the underlying data distribution, making it more flexible in handling diverse types of datasets.

Disadvantages of K Nearest Neighbour(KNN)

• Computational Complexity: As the size of the dataset grows, the computation required to find the nearest neighbors increases, leading to higher computational costs.
• Sensitivity to Outliers: KNN is sensitive to outliers, as they can significantly affect the distances between points and, consequently, the predictions.
• Dimensionality: KNN performs poorly in high-dimensional spaces, as the concept of proximity becomes less meaningful in higher dimensions (curse of dimensionality).

Support Vector Machine vs K Nearest Neighbours

Aspect	Support Vector Machine(SVM)	K Nearest Neighbour(KNN)
Basis	Based on optimal hyperplane that maximizes the margin between classes.	Classify a data point based on the majority class of its nearest neighbours.
Algorithm Type	Discriminative algorithm, learn by analyzing the differences between classes.	Lazy Learning, stores all the training data and classifies new points based on their similarity to the stored data points at prediction time.
Training Type	Slower	Faster as there is no explicit learning
Decision Boundary	Can be either linear or non-linear	Non-linear
Memory Usage	Lower	Flexible, adapts to data distribution
Prediction Time	Faster	Slower with large dataset
Performance with Imbalanced Data	Handles well	May or may not perform well
Hyperparameter Sensitivity	Very less parameters	More parameters depending on the value of K
Interpretability	Less interpretable,due to the complex nature of their decision boundaries	More interpretable,because the classification relies on the nearest neighbors.
Scalability	More scalable	Less scalable
Example	Text Classification, Image classification, Bioinformatics, etc.	Recommendation Systems, Anomaly Detection, Predictive maintenance, etc.

Conclusion

In practical use, KNN and SVM both are very important supervised learning algorithms. The choice between these two deoends on multiple factors like the nature of dataset, the size of dataset and other specifications of the problem. Ultimately the selection must be guided by the constraints of the problem.